How to Build an ETL App for Aircall Data in Python with CData

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData API Driver for Python and the petl framework, you can build Aircall-connected applications and pipelines for extracting, transforming, and loading Aircall data. This article shows how to connect to Aircall with the CData Python Connector and use petl and pandas to extract, transform, and load Aircall data.

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Aircall data in Python. When you issue complex SQL queries from Aircall, the driver pushes supported SQL operations, like filters and aggregations, directly to Aircall and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Aircall Data

Connecting to Aircall data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

Start by setting the Profile connection property to the location of the Aircall Profile on disk (e.g. C:\profiles\Aircall.apip). Next, set the ProfileSettings connection property to the connection string for Aircall (see below).

Aircall API Profile Settings

In Aircall, go to your company settings and create a new API key to receive an API ID and API token. Combine them as

APIId:APIToken

After installing the CData Aircall Connector, follow the procedure below to install the other required modules and start accessing Aircall through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install petl
pip install pandas

Build an ETL App for Aircall Data in Python

Once the required modules and frameworks are installed, we are ready to build our ETL app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import petl as etl
import pandas as pd
import cdata.api as mod

You can now connect with a connection string. Use the connect function for the CData Aircall Connector to create a connection for working with Aircall data.

cnxn = mod.connect("Profile=C:\profiles\Aircall.apip;ProfileSettings='APIKey=your_api_id:your_api_token';")

Create a SQL Statement to Query Aircall

Use SQL to create a statement for querying Aircall. In this article, we read data from the Calls entity.

sql = "SELECT Id, Direction FROM Calls WHERE Status = 'completed'"

Extract, Transform, and Load the Aircall Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the Aircall data. In this example, we extract Aircall data, sort the data by the Direction column, and load the data into a CSV file.

Loading Aircall Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'Direction')

etl.tocsv(table2,'calls_data.csv')

With the CData API Driver for Python, you can work with Aircall data just like you would with any database, including direct access to data in ETL packages like petl.

Free Trial & More Information

Download a free, 30-day trial of the CData API Driver for Python to start building Python apps and scripts with connectivity to Aircall data. Reach out to our Support Team if you have any questions.

Full Source Code

import petl as etl
import pandas as pd
import cdata.api as mod

cnxn = mod.connect("Profile=C:\profiles\Aircall.apip;ProfileSettings='APIKey=your_api_id:your_api_token';")

sql = "SELECT Id, Direction FROM Calls WHERE Status = 'completed'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'Direction')

etl.tocsv(table2,'calls_data.csv')